如何在GPU上运行预先训练好的pytorch模型?
在这里,我尝试使用mobilenetv2 mobile在自定义数据集上进行训练。我可以让它在CPU上工作,但我更喜欢在GPU上运行。相反,我得到了如下错误: RuntimeError:应为后端CPU的对象,但为参数#2“weight”获取了后端CUDA RuntimeError:应为后端CPU的对象,但为参数#4'mat1获取了后端CUDA 就像我的帖子问的那样,我怎样才能让预先训练好的模型在GPU上运行如何在GPU上运行预先训练好的pytorch模型?,pytorch,Pytorch,在这里,我尝试使用mobilenetv2 mobile在自定义数据集上进行训练。我可以让它在CPU上工作,但我更喜欢在GPU上运行。相反,我得到了如下错误: RuntimeError:应为后端CPU的对象,但为参数#2“weight”获取了后端CUDA RuntimeError:应为后端CPU的对象,但为参数#4'mat1获取了后端CUDA 就像我的帖子问的那样,我怎样才能让预先训练好的模型在GPU上运行 MobileNet = models.mobilenet_v2(pretrained =
MobileNet = models.mobilenet_v2(pretrained = True)
if torch.cuda.is_available():
MobileNet.cuda()
for param in MobileNet.parameters():
param.requires_grad = False
torch.manual_seed(50)
MobileNet.classifier = nn.Sequential(nn.Linear(1280, 1000), nn.ReLU(), nn.Dropout(0.5), nn.Linear(1000,3), nn.LogSoftmax(dim=1))
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(MobileNet.classifier.parameters(), lr=0.001)
train_transform = transforms.Compose([
transforms.RandomRotation(10), # rotate +/- 10 degrees
transforms.RandomHorizontalFlip(), # reverse 50% of images
transforms.Resize(224), # resize shortest side to 224 pixels
transforms.CenterCrop(224), # crop longest side to 224 pixels at center
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
test_transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
train_data = datasets.ImageFolder('C:/Users/mixv/Pictures/Summer/datasets/train', transform=train_transform)
test_data = datasets.ImageFolder('C:/Users/mix/Pictures/Summer/datasets/test', transform=test_transform)
torch.manual_seed(42)
batch=64
train_loader = DataLoader(train_data, batch_size=batch, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch, shuffle=True)
if torch.cuda.is_available():
train_loader = DataLoader(train_data, batch_size=batch, shuffle=True, pin_memory = True)
test_loader = DataLoader(test_data, batch_size=batch, shuffle=True, pin_memory = True)
epochs = 10
train_losses = []
test_losses = []
train_correct = []
test_correct = []
start_time =time.time()
for i in range(epochs):
trn_corr = 0
tst_corr = 0
# Run the training batches
for b, (images, labels) in enumerate(train_loader):
if torch.cuda.is_available():
images = images.cuda()
labels = labels.cuda()
b+=1
# Apply the model
y_pred = MobileNet(images)
loss = criterion(y_pred, labels)
# Tally the number of correct predictions
predicted = torch.max(y_pred.data, 1)[1]
batch_corr = (predicted == labels).sum()
trn_corr += batch_corr
accuracy = trn_corr.item()*100/(b*batch)
# Update parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()
正如RuntimeError所说,一些权重仍然在cpu中。我怀疑的一个可能的缺陷是
MobileNet.classifier=nn.Sequential(nn.Linear(12801000)、nn.ReLU()、nn.Dropout(0.5)、nn.Linear(1000,3)、nn.LogSoftmax(dim=1))
是在MobileNet.cuda()
之后完成的,这意味着这些新创建的权重可能没有发送到gpu。试着把这两个人的顺序颠倒过来,看看非常感谢。这样一个简单的错误让我困了好几个小时。